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The heat-treatable AA 2024-T6 has been used widely in the aerospace and defense industry. In this paper, the effective parameters of the double-sided friction stir welding (DFSW) process of AA 2024-T6 were investigated by using Response Surface Methodology (RSM). Also, the optimal parameters have been obtained by using the Artificial Neural Network (ANN) and optimization algorithms. The most significant process parameters, i.e. rotational speed, welding speed, tilt angle, and tool pin length, were considered to predict the behavior of the response, i.e. ultimate tensile strength (UTS) and percentage elongation (% El). For this purpose, a central composite design, as well as analysis of variance, was used to investigate effective parameters. Also, an intelligent connection between the above parameters and responses was developed by using ANN. The optimal neural network was integrated with the whale optimization algorithm (WOA) to find optimal parameters and achieve desirable responses. The obtained result was verified by experiment. Results indicated that the tool pin length (h) factor has the highest contribution percentage between the parameters (14.95% and 9.59% for UTS and %El, respectively). Besides, the parameters of welding speed (v) and tilt angle (α) have a significant effect on the UTS and %El of DFSW joints, respectively. Using a rotational speed of 1250 rpm, welding speed of 80 mm/min, the tilt angle of 4°, and a tool pin length of 2.3 mm, as optimal parameters of the DFSW process, can be achieved the maximum of ultimate tensile strength and elongation. [ABSTRACT FROM AUTHOR]

This paper focuses on training the mathematical models for prediction of optimal livestock feed blend for animal. In this paper, three functions are trained to find the most optimal percentage of price, nutrients and water. For this purpose three objective functions has been prepared using corresponding values of feed ingredients and an algorithm is developed using ANN. The objective functions are trained with 5000 random values to find the optimized results and prediction ability is compared with existing model. Developed algorithm shows superiority over the existing programming models. [ABSTRACT FROM AUTHOR]

The article cites studies on the evaluation of mathematical models that are capable of predicting the outlet kappa number of the delignification reactor of one of the paper mills in Brazil. The author insists that the applications of these models to process simulation, optimization, and control are a useful tool for cellulose mills. The study suggests three kinetic models and has identified nonlinear models for the delignification plant. It reveals that the models can be applied to simulate daily processes and can be used as a performance indicator of the process.

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural network to further improve the accuracy of the approach while minimizing the cost of crowdsourcing. The paper also discusses the data pre-processing steps used for training the convolutional neural network. Finally it describes the airplane sensor dataset which is used for demonstration of this approach and then shows the experimental results achieved using convolutional neural network. [ABSTRACT FROM AUTHOR]

Abstract This paper provides a starting point for the development of metacognition in a common model of cognition. It identifies significant theoretical work on metacognition from multiple disciplines that the authors believe worthy of consideration. After first defining cognition and metacognition, we outline three general categories of metacognition, provide an initial list of its main components, consider the more difficult problem of consciousness, and present examples of prominent artificial systems that have implemented metacognitive components. Finally, we identify pressing design issues for the future. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • An improved LM, incorporating an adaptive learning rate strategy into the learning process, is developed to improve the modeling performance. • The structure of RRBFNN can be self-organized by using the efficient information-oriented algorithm during the learning process. • The convergence of IOA-RRBFNN has been demonstrated theoretically and experimentally. Abstract In this paper, an efficient self-organizing recurrent radial basis function neural network (RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a two-steps learning approach is introduced during the learning process. In the first step, the objective is to find the optimal set of parameters using an improved Levenberg-Marquardt (LM) algorithm. In the second step, an efficient information-oriented algorithm (IOA), without any thresholds, is developed to optimize the structure of RRBFNN. The hidden neurons in this IOA-based RRBFNN (IOA-RRBFNN) are generated or pruned automatically to reduce the computational complexity and improve the generalization power. Meanwhile, a theoretical analysis on the learning convergence of IOA-RRBFNN is given in details. To demonstrate the merits of IOA-RRBFNN for modeling nonlinear systems, several benchmark problems and a real world application are present with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed IOA-RRBFNN performs prediction accuracy in the case of fast learning speed and compact structure. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel divide-and-conquer model for CPI predictionwith the existing compilation method of the Consumer Price Index (CPI) in China. Historical national CPI time series is preliminary divided into eight sub-indexes includingfood, articles for smoking and drinking, clothing, household facilities, articles and maintenance services, health care and personal articles, transportation and communication, recreation, education and culture articles and services, and residence. Three models including back propagation neural network (BPNN) model, grey forecasting model (GM (1, 1)) and autoregressive integrated moving average (ARIMA) model are established to predict each sub-index, respectively.Then the best predicting result among the three models’ for each sub-index is identified. To further improve the performance, special modification in predicting method is done to sub-CPIs whose forecasting results are not satisfying enough. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs.Eventually, the best predicting results of each sub-index are integrated to form the forecasting results of the national CPI. Empirical analysis demonstrates that the accuracy and stability of the introduced method in this paper is better than many commonly adopted forecasting methods, which indicates the proposed method is an effective and alternative one for national CPI prediction in China. [Copyright &y& Elsevier]

In the largest samplings of data, outliers are observations that are well separated from the major samples. To deal with outlier problems, a least trimmed squares (LTS) estimator is developed for robust linear regression problems. It is meaningful to generalize the LTS estimator to fuzzy neural network (FNN) for robust nonlinear regression problems. In addition, the determination of the trimming constant is important when using the LTS estimator. In this paper, we propose the use of an adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies a scale estimate to a LTS-FNN. This paper particularly emphasizes the robustness of the proposed network against outliers and an automatic determination of the trimming percentage. Simulation problems are provided to compare the performance of the proposed ALTS-FNN, with an LTS-FNN and typical FNN. Simulation results show that the proposed ALTS-FNN is highly robust against outliers. [ABSTRACT FROM AUTHOR]

Nowadays, more and more object recognition tasks are being solved with Convolutional Neural Networks (CNN). Due to its high recognition rate and fast execution, the convolutional neural networks have enhanced most of computer vision tasks, both existing and new ones. In this article, we propose an implementation of traffic signs recognition algorithm using a convolution neural network. The paper also shows several CNN architectures, which are compared to each other. Training of the neural network is implemented using the TensorFlow library and massively parallel architecture for multithreaded programming CUDA. The entire procedure for traffic sign detection and recognition is executed in real time on a mobile GPU. The experimental results confirmed high efficiency of the developed computer vision system. [ABSTRACT FROM AUTHOR]

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. [ABSTRACT FROM AUTHOR]

In the present world of ‘Big Data,’ the communication channels are always remaining busy and overloaded to transfer quintillion bytes of information. To design an effective equalizer to prevent the inter-symbol interference in such scenario is a challenging task. In this paper, we develop equalizers based on a nonlinear neural structure (wavelet neural network (WNN)) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm). The performance of the proposed equalizer is compared with WNN trained by cat swarm optimization (CSO) and clonal selection algorithm (CLONAL), particle swarm optimization (PSO) and least mean square algorithm (LMS). The performance is also compared with other equalizers with structure based on functional link artificial neural network (trigonometric FLANN), radial basis function network (RBF) and finite impulse response filter (FIR). The superior performance is demonstrated on equalization of two non-linear three taps channels and a linear twenty-three taps telephonic channel. It is observed that the performance of the gradient algorithm based equalizers fails in the presence of burst error. The robustness in the performance of the proposed equalizers to handle the burst error conditions is also demonstrated. [ABSTRACT FROM AUTHOR]

Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs. This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs. [ABSTRACT FROM AUTHOR]

In this paper, a new multimodel approach for complex systems modeling based on classification algorithms is presented. It requires firstly the determination of the model-base. For this, the number of models is selected via a neural network and a rival penalized competitive learning (RPCL), and the operating clusters are identified by using the fuzzy K-means algorithm. The obtained results are then exploited for the parametric identification of the models. The second step consists in validating the proposed model-base by using the adequate method of validity computation. Two examples are presented in this paper which show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: In-situ stress field is very important in the numerical simulation and stability analysis as well as in the engineering design and construction. So it requests an effective analysis method. The typical in-situ stress analysis method is multivariate regression, which is based on 3-D FEM direct modeling, but it may be not so accurate to obtain the distribution of in-situ stress field of the project, because when the multivariate regression is carried out, the cross influence of the main factors of in-situ stress field is ignored, and the relationship of in-situ stress field and the main factors is too complicated to express by subharmonic multivariate function. This paper presents an improved method back propagation (BP) neural network back analysis of in-situ stress field, based on the calculated results of multivariate linear regression analysis. For in the ANN (Artificial Neural Networks) method, the training samples are difficult to generate, while combining the linear regression method with ANN method this problem can be solved perfectly. Linear elastic multivariate regression is carried out to determine the general bounds of optimized paraments, and uniform design method is carried out to settle different combinations of factor levels. Training samples are gained by FEM analysis. And the visual neural network by using ActiveX technology is realized in this paper, by taking full advantages of strong capability in computing of Matlab and features of friendly interface of VB perfectly. In fact, one needs to know the status of the in-situ stress field at some important location in the engineering, by using neural network method, intelligent expression method of the in-situ stress field is presented, and the developed man-computer interface has the property of easy use in the actual engineering. [Copyright &y& Elsevier]

Abstract: This paper studies the vehicle rollover warning based on the vehicle running state parameters which are easy for collecting. By taking advantages of neural network and support vector machine, this paper analyzes the probabilities of the vehicle rollover in the scenario of single lane change and establishes a warning model which could predict the rollover 0.4seconds in advance for a particular type of vehicle. According to the validation experiments on Ve- DYNA which is a vehicle dynamic simulation software, the results validate the warning model''s good performance with the maximum error as 0.04 second and no missing cases which indicates its validity for rollover warning. [Copyright &y& Elsevier]

In this paper, we have made an attempt to estimate the urban Orientation Distribution (OD) matrix for solving the complexity of the Urban transport network using the mathematical model for real data. The Hopfield Neural Network (HNN) was used to solve the travelling salesman problem, and the model was tested with artificial data which performed reasonably well. The paper concentrates on the estimation of trip matrices from link volume counts. [ABSTRACT FROM AUTHOR]

In the automotive industry, engine test engineers are required to deal with a huge quantity of experimental data obtained from engine test beds each day. Those data must be analysed to evaluate engine performance and to guide further engine test operations. In order to improve efficiency and reduce expenditure of time in engine testing, it is very important for engine test bed controllers to develop a mathematical model from existing engine test data. This paper presents an investigation of a neural network-genetic algorithm (GA) combined tool for engine modelling. In the modelling tool, a real-coded GA has been employed to train three different groups of neural networks (a multilayer perceptron group, a radial basis function group, and a bar function networks group) and then finally to find the most suitable neural network model for engine modelling. The experimental results given in this paper show that the proposed tool has been successfully used for Rover engine testing. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space. The proposed algorithm employs a fuzzy inference system to consider all the partial performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m 2 m −2 , defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm −2 , defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R 2 = 0.64 and RMSE = 42.67 gm −2 ) than the exponential regression (R 2 = 0.48 and RMSE = 41.65 gm −2 ) and the ANN (R 2 = 0.43 and RMSE = 46.26 gm −2 ). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R 2 = 0.55) but higher RMSE (RMSE = 37.79 gm −2 ). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology. [ABSTRACT FROM AUTHOR]

The support vector machine, as a powerful machine learning algorithm, is recognized to have good generalization ability in its application to multi-class machine-fault classification problems. In this paper, an application of the SVM in the multi-class gear-fault diagnosis has been performed based on the gear vibration data in time domain. From time domain data the statistical features are extracted and fed to the SVM for the training/testing. When the training and testing data are at the same running speed, it is found that the SVM classifier has excellent multiclass classification accuracy. Though, this approach relies on the availability of both the training and testing data at that particular speed of the machine operation; it is expected that features of time domain data would change with operating speeds. Moreover, the training data may not always be available continuously at all operating speeds of gears, especially for variable-drive systems. Thus, a novel technique of interpolation/extrapolation has been proposed in the present work that helps the SVM classifier to carryout multi-class gear-fault diagnosis with appreciable accuracy even in the absence of the training data at a given running speed. It also investigates the speed bandwidth of the training data for which interpolation/extrapolation predictions are reasonably good. The similar analysis is also extended to artificial neural networks (ANN) as the classifier and predictions are noted, and compared with the SVM. [ABSTRACT FROM AUTHOR]

In order to improve the real time performance, accuracy and stability, an obstacle avoidance strategy of mobile robot based on wireless sensor networks is proposed in this paper. The algorithm is combined with fuzzy control and neural network control theory which can express those rules difficult to describe by using mathematical model. Simulation results show that the obstacle avoidance strategy can improve the overall system performance substantially and is suitable for mobile robot intelligent control. [ABSTRACT FROM AUTHOR]

Cogeneration is the simultaneous generation of electricity and useful heat with the aim of exploiting more efficiently the energy stored in the fuel. Cogeneration is, however, a complex process that encompasses a great amount of sub-systems and variables. This fact makes it very difficult to obtain an analytical model of the whole plant, and therefore providing a mechanism or a methodology able to optimize the global behavior. This paper proposes a neuro-genetic strategy for modeling and optimizing a cogeneration process of a real industrial plant. Firstly, the modeling of the process is carried out by means of several interconnected neural networks where, each neural network deals with a particular sub-system of the plant. Next, the obtained models are used by a genetic algorithm, which solves a multiobjective optimization problem of the plant, where the goal is to minimize the fuel consumption and maximize both the generated electricity and the use of the heat. The proposed approach is evaluated with data of a real cogeneration plant collected over a one-year period. Obtained results show not only that the modeling of the plant is correct but also that the optimization increases significantly the efficiency of the cogeneration plant. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a generalized approach to state-feedback stabilization of interconnected fuzzy systems. The proposed algorithms, which are formulated within the convex optimization framework, provide decentralized solutions to the problem of asymptotic stability with strict dissipativity. It is established that the new methodology can reproduce earlier results on passivity, positive realness, and disturbance attenuation. Numerical examples are presented to illustrate the applicability of the design method. [ABSTRACT FROM AUTHOR]

This paper presents a neural network algorithm called SOLAR (Supervised One-shot Learning Algorithm for Real-valued inputs) that can complete learning by a single presentation of the real-valued inputs. The similarity matrix is introduced in SOLAR, which can measure the Euclidean distance between training instances represented by a real number. Based on the geometrical structure of the similarity matrix, the structure of the network, the connection weights, the learning parameters and the linear disciminant function are determined. Then, the learning is executed by a single presentation of the training set. The nonlinearly separable case by decomposing the set of supervisor signals into linearly separable sets. The main contribution of this paper is that SOLAR realizes a high-speed learning by a single presentation of the input data composed of real values and a large-scale complex set of data can be learned. This has been difficult in previous models. In addition, since the number of hidden units and the parameters are determined by the algorithm, there is no problem such as trial and error. [ABSTRACT FROM AUTHOR]

Kohomen's model as the self-organizing model for the neural network can be considered as a kind of adaptive vector quantization algorithm. Numerous reports have been presented on the application of the model to practical problems. Although some results have been presented for the theoretical properties of Kohonen's model, many properties remain to be clarified. Among various properties of Kohonen's model as an adaptive vector quantization algorithm, this paper considers the problem of how the reference vectors are placed according to the probability distribution of the input signal. Considering the limit where the number of reference vectors is increased to infinity, this problem can be discussed theoretically as the distribution of the reference vectors. Due to the effect of the "learning by neighborhood," which is the feature of Kohonen's model, the property of the Kohonen model differs quantitatively from the property of the ordinary vector quantization algorithm. This paper discusses quantitatively the properties of Kohonen's model using the average learning equation. [ABSTRACT FROM AUTHOR]

Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later. [ABSTRACT FROM AUTHOR]

Even though the number and variety of fuel consumption models projected in the literature are common, studies on their validation using real-life data is not only limited but also does not fit well with the real-time data. In this paper, three statistical models namely Support Vector Machine (SVM), Artificial Neural Network and Multiple Linear Regression are used in term of prediction of total and instant fuel consumption. The models are compared against data collected in real-time from three different passenger vehicles on three routes by causal drive, using a mobile phone application. Our outcomes reveal that, the results obtained by the models vary depending on the total consumption and instant consumption correlation. Support Vector Machine model of fuel consumption expose comparatively better correlation than the other statistical fuel consumption models. [ABSTRACT FROM AUTHOR]

Choice-based network revenue management concentrates on importing choice models within the traditional revenue management system. Multinomial logit is a popular and well-known model which is the basic choice model in revenue management. Empirical results indicate inadequacy of this model for predicting itinerary shares; therefore, more realistic models, such as nested logit, can be proposed for substituting it. Incorporating complex choice models in the optimization module based on statistical tests without considering the complexity of the obtained mathematical model would lead to increase in the complexity of a system without obtaining significant improvement. Considering the influence of discrete choice model on the structure of optimization model, it is necessary to analyze the interaction between specific discrete choice and optimization models. In this paper, a knowledge acquisition subsystem is introduced for providing intelligence and considering the most suitable choice models. We develop the feedforward multilayer perceptron artificial neural network for forecasting revenue improvement percent obtained by using more realistic choice models. The obtained results demonstrate that the new system will decrease tile complexity of tile system, simultaneously, while preserving revenue of the firm. According to the computational results, by increasing the resource restriction, the process of incorporating more realistic choice model will be more important. [ABSTRACT FROM AUTHOR]

Supply Chain consists of various components like supplier, manufacturer, factories, warehouses, distribution agents, customers, etc. Supply Chain Management encompasses all the activities from moving goods from sourcing to consumption. Sales forecasting is a part of downstream activity of supply chain and is the process of predicting future sales of the product. It helps in making informed business decisions. In this paper a study of various sales forecasting algorithms is done and results of sales of oral-care products are calculated using Back-Propagation Neural Network Model. The error rate for different products is also calculated. [ABSTRACT FROM AUTHOR]

The success of an artificial neural network (ANN) strongly depends on the variety of the connection weights and the network structure. Among many methods used in the literature to accurately select the network weights or structure in isolate; a few researchers have attempted to select both the weights and structure of ANN automatically by using metaheuristic algorithms. This paper proposes modified bat algorithm with a new solution representation for both optimizing the weights and structure of ANNs. The algorithm, which is based on the echolocation behaviour of bats, combines the advantages of population-based and local search algorithms. In this work, ability of the basic bat algorithm and some modified versions which are based on the consideration of the personal best solution in the velocity adjustment, the mean of personal best and global best solutions through velocity adjustment and the employment of three chaotic maps are investigated. These modifications are aimed to improve the exploration and exploitation capability of bat algorithm. Different versions of the proposed bat algorithm are incorporated to handle the selection of the structure as well as weights and biases of the ANN during the training process. We then use the Taguchi method to tune the parameters of the algorithm that demonstrates the best ability compared to the other versions. Six classifications and two time series benchmark datasets are used to test the performance of the proposed approach in terms of classification and prediction accuracy. Statistical tests demonstrate that the proposed method generates some of the best results in comparison with the latest methods in the literature. Finally, our best method is applied to a real-world problem, namely to predict the future values of rainfall data and the results show satisfactory of the method. [ABSTRACT FROM AUTHOR]

This paper presents a biologically inspired, sequential learning spiking neural classifier (SLSNC) for pattern classification problems. It consists of a two layered neural network and a separate decision block which estimates the predicted class label. Inspired by observations in the neuroscience literature, the input layer employs a new neuron model which converts real valued stimuli into spikes with varying amplitudes and firing times. The intermediate layer neurons are modeled as integrate-and-fire spiking neurons. The decision block identifies that intermediate neuron which fires first and returns the class label associated with that neuron as the predicted class label. The sequential learning algorithm for the spiking neural network automatically determines the network structure from the training samples and adapts its synaptic weights by long term potentiation and long term depression. Performance of SLSNC has been evaluated using a number of benchmark classification problems and the results have been compared with other well-known spiking neural network classifiers in the literature as well as with the standard support vector machine (SVM) with a Gaussian kernel and the fast learning Extreme Learning Machine (ELM) classifiers. The results clearly indicate that the described spiking neural network produces similar or better generalization performance with a smaller network. [ABSTRACT FROM AUTHOR]

Estimates of suspended sediment concentrations and transport are an important part of any marine environment assessment study because these factors have a direct impact on the life cycle and survival of marine ecosystems. This paper proposes to implement a combined methodology to tackle these estimates. The first component of the methodology comprised two numerical current and wave models, while the second component was based on the artificial intelligence technique of neural networks (ANNs) used to reproduce values of sediment concentrations observed at two sites. The ANNs were fed with modelled currents and waves and trained to produce area-specific concentration estimates. The trained ANNs were then applied to predict sediment concentrations over an independent period of observations. The use of a data set that merged together observations from both the mentioned sites provided the best ANN testing results in terms of both the normalised root mean square error (0.13) and the mean relative error (0.02). [ABSTRACT FROM AUTHOR]

This paper presents a Wavelet-based Artificial Neural Network (WANN) approach to model rainfall-runoff process of the Delaney Creek and Payne Creek watersheds with distinct hydro-geomorphological characteristics, located in Florida. Wavelet is utilized to handle the multi-frequency characteristics of the process in daily and monthly time scales. Thus, rainfall and runoff time series were decomposed into several sub-series by various mother wavelets. Due to multiple components obtained through wavelet decomposition, input sets to the Feed Forward Neural Network (FFNN) were enhanced. The application of two information content based criteria (i.e., entropy, H, and mutual information, MI) to select more reliable input sets (among all potential input sets) and to have better insight into the physics of process is considered as the basic innovation of the study which led to a more accurate and compact model. The increase in the number of input of the FFNN might lead to a complex structure and low performance. The results demonstrated that MI as a supervised feature extraction criterion could lead to more reliable outcomes due to its non-linear nature. Furthermore, results indicated the superiority of proposed entropy-based WANN model (EWANN) in comparison to simple FFNN. Moreover, multi-step-ahead FFNN, conventional WANN and classic Auto Regressive Integrated Moving Average with eXogenous inputs (ARIMAX) models could not reveal appropriate forecasting results with regard to EWANN model. The superiority of the EWANN over the WANN and FFNN models is not only in terms of efficiency criteria, but also due to its appropriate ability to provide information about the physics of the process. The consequences of EWANN for rainfall-runoff modeling of two watersheds revealed that the proposed EWANN could simulate the process of a small and flat sub-basin slightly reliable than a sloppy and wild watershed. The poor outcome of monthly modeling in regard to daily modeling might be due to involvement of more uncertainty in the monthly data. [ABSTRACT FROM AUTHOR]

The electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly. [ABSTRACT FROM AUTHOR]

This work presents a real time testing approach of an Intelligent Multiobjective Nonlinear-Model Predictive Control Strategy (iMO-NMPC). The goal is the testing and analysis of the feasibility and reliability of some Soft Computing (SC) techniques running on a real time industrial controller. In this predictive control strategy, a Multiobjective Genetic Algorithm is used together with a Recurrent Artificial Neural Network in order to obtain the control action at each sampling time. The entire development process, from the numeric simulation of the control scheme to its implementation and testing on a PC-based industrial controller, is also presented in this paper. The computational time requirements are discussed as well. The obtained results show that the SC techniques can be considered also to tackle highly nonlinear and coupled complex control problems in real time, thus optimising and enhancing the response of the control loop. Therefore this work is a contribution to spread the SC techniques in on-line control applications, where currently they are relegated mainly to be used off-line, as is the case of optimal tuning of control strategies. [ABSTRACT FROM AUTHOR]

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models. [ABSTRACT FROM AUTHOR]

Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox. [ABSTRACT FROM AUTHOR]

Echo state networks (ESN) or reservoirs, are random, recurrent neural network topologies that integrate temporal data over short time windows by operating on the edge of chaos. Recently, there is a significant effort on the mathematical modeling and software topologies of the reservoirs. However, hardware reservoir fabrics are essential to deploy in energy constrained environments. In this paper, we investigate a hardware reservoir with bi-stable memristive synapses. In particular, we demonstrate a scalable hardware model for detecting real-time epileptic seizures in human models. The performance of the proposed reservoir hardware is evaluated for absent seizure signals with 85% accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers. [ABSTRACT FROM AUTHOR]

Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use. [ABSTRACT FROM AUTHOR]

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANN), which are used in many different areas, have been applied to predict the vertical swelling percentage of expansive clays. In contrast to previous models that estimated ANN Models in a single phase, this paper proposes an alternative analysis in based on the following two-stage operation: (a) conducting an ANN analysis on the swelling-pressure test results (i.e., the ASTM 4546 Method C test results) to obtain the swelling- pressure model for any given clay characteristics, and (b) performing an additional ANN analysis on the swelling-percentage test results (i.e., the ASTM 4546 Method B test results), including the former ones, with the given independent variables of the clay characteristics. This second stage includes a defined expression containing the given surcharge pressure and the predicted value of the swelling pressure as obtained from the model of the previous stage. Two final ANN Models, each with a different arrangement of the given independent variables, were derived from this two-stage procedure. Their statistical fit was clearly found to be superior in comparison to previous models estimated with the same data set. Furthermore, one of these two models exhibited the expected geophysical behavior. As this new ANN Model yields higher predicted swelling-percentage values, it can definitely be regarded as a preferable one in the sense of enlarging the safety margin in heave calculations. [ABSTRACT FROM AUTHOR]

Vibrato is an important factor that affects the naturalness level of a synthetic singing voice. Therefore, the analysis and modeling of vibrato parameters are studied in this paper. The vibrato parameters of those syllables segmented from recorded songs are analyzed by using short-time Fourier transform and the method of analytic signal. After the vibrato parameter values for all training syllables are extracted and normalized, they are used to train an artificial neural network (ANN) for each type of vibrato parameter. Then, these ANN models are used to generate the values of vibrato parameters. Next, these parameter values and other music information are used together to control a harmonic-plus-noise model (HNM) to synthesize Mandarin singing voice signals. With the synthetic singing voice, subjective perception tests are conducted. The results show that the singing voice synthesized with the ANN generated vibrato parameters is much increased in the naturalness level. Therefore, the combination of the ANN vibrato models and the HNM signal model is not only feasible for singing voice synthesis but also convenient to provide multiple singing voice timbres. [ABSTRACT FROM AUTHOR]

This paper presents a Markovian model for Flexible Manufacturing Systems (FMSs). The model considers two features of automated exible manufacturing systems equipped with the Automated Guided Vehicle (AGV), namely the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. Performance measure is a critical factor used to judge the effectiveness of a manufacturing system. The studies in the literature did not compare Markovian and neural networks, especially in the reliability modeling of an advanced manufacturing system, considering AGVs. The current methods for modeling the reliability of a system involve determination of system state probabilities and transition states. Since the failure of the machines and AGVs could be considered in different states, a Markovian model is proposed for reliability assessment. Also, a neural network model is developed to point out the difference in the accuracy of the Markovian model in comparison with the neural network. The optimization objectives in the proposed model are maximizing the total reliability of machines in shops in the whole jobshop system and maximizing the total reliability of the AGVs. The multi-objective mathematical model is optimized using an analytic hierarchy process. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the formation control of multi-robot systems, where the kinematic model of a differentially driven wheeled mobile robot is considered. Based on the graph-theoretic concepts and locally distributed information, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. In addition to simulations, the proposed techniques are applied to an experimental multi-robot platform for performance validations. From simulation and experimental results, the proposed adaptive neural fuzzy protocol can provide better formation responses compared to conventional consensus algorithms. [ABSTRACT FROM AUTHOR]

The electro-hydraulic proportional position servo system is a kind of highly non-linear and time-varying system as well as parameter uncertainties. It is often difficult to achieve satisfactory control results with the traditional control methods. Considering that the adaptive neural network PID control method has the characteristics of online learning and adjustment PID control parameters adaptively, this paper puts forward to adopt BP-PID control method for the position servo control of electro-hydraulic proportional system. An online adaptive BP-PID controller is designed. The simulation model of the system is established based on Simulink platform. The step response and Sinusoidal tracking characteristics are researched through simulations and experiments. The results show that BP-PID has a strong ability of online learning and self-tuning PID parameters. Compared with the traditional PID control method, the BP-PID control has strong self-adaptability and robustness, and can significantly improve the step response speed and tracking control precision. [ABSTRACT FROM AUTHOR]